2020 International Society of Hypertension Global Hypertension Practice Guidelines
Bibliographic record
Abstract
Context and Purpose of This Guideline \nStatement of Remit \nTo align with its mission to reduce the global burden of raised \nblood pressure (BP), the International Society of Hypertension \n(ISH) has developed worldwide practice guidelines for the \nmanagement of hypertension in adults, aged 18 years and \nolder. \nThe ISH Guidelines Committee extracted evidence-based \ncontent presented in recently published extensively reviewed \nguidelines and tailored and standards \nof care in a practical format that is easy-to-use particularly \nin low, but also in high resource settings – by clinicians, but \nalso nurses and community health workers, as appropriate. \nAlthough distinction between low and high resource settings \noften refers to high (HIC) and low- and middle-income countries (LMIC), it is well established that in HIC there are areas \nwith low resource settings, and vice versa. \nHerein optimal care refers to evidence-based standard of \ncare articulated in recent guidelines1,2 and summarized here, \nwhereas standards recognize that \nstandards would not always be possible. Hence essential standards refer to minimum standards of care. To allow specification of essential standards of care for low resource settings, \nthe Committee was often confronted with the limitation or \nabsence in clinical evidence, and thus applied expert opinion
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.085 | 0.069 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".